Caller-REP: Detecting unwanted calls with caller social strength

Voice over IP (VoIP) is a cost effective mechanism for telemarketers and criminals to generate bulk spam calls. A challenge in managing a VoIP network is to detect spam calls without user involvement or content analysis. In this paper we present a novel content independent, non-intrusive approach based on caller trust and reputation to block spam callers in a VoIP network. Our approach uses call duration, interaction rate, and caller out-degree distribution to establish a trust network between VoIP users and computes the global reputation of a caller across the network. Our approach uses historical information for automatically determining a global reputation threshold below which a caller is declared as socially non-connected and as a spammer. No VoIP data-set is available for testing the detection mechanism. We verify the accuracy of our approach with synthetic data that we generate by randomly varying the call duration, call rate, and out-degree distributions of spammers and legitimate users. This evaluation shows that our approach can automatically detect spam callers in a network. Our approach achieves a false positive rate of less than 10% and true positive rate of almost 80% in the first two days even in the presence of a significant number of spammers. This increases to a true positive rate of 99% and drops a false positive rate to less than 2% on the third day. In a network with no spammers, our approach achieves a false positive rate of less than 10%. In a network heavily saturated with more than 60% of spam callers, our approach achieves a true positive rate of 98% and no false positives. We compare the performance of our approach with a closely related spam detection approach named Call-Rank. The results show that our approach outperforms Call-Rank in terms of detection accuracy and detection time.